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A spatial regularization approach for vector quantization

Abstract : Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.
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Submitted on : Thursday, October 28, 2010 - 4:51:35 PM
Last modification on : Saturday, January 15, 2022 - 3:58:14 AM
Long-term archiving on: : Saturday, January 29, 2011 - 2:50:28 AM


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  • HAL Id : hal-00530369, version 1


Caroline Chaux, Anna Jezierska, Jean-Christophe Pesquet, Hugues Talbot. A spatial regularization approach for vector quantization. Journal of Mathematical Imaging and Vision, Springer Verlag, 2011, 41, pp.23-38. ⟨hal-00530369⟩



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